> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-studio-tools-doc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Document Chunking

Document chunking is a method of splitting documents into smaller chunks based on document structure like paragraphs and sections. It analyzes natural document boundaries rather than splitting at fixed character counts. This is useful when you want to process large documents while preserving semantic meaning and context.

<Steps>
  <Step title="Create a Python file">
    ```python document_chunking.py theme={null}
    import asyncio
    from agno.agent import Agent
    from agno.knowledge.chunking.document import DocumentChunking
    from agno.knowledge.knowledge import Knowledge
    from agno.knowledge.reader.pdf_reader import PDFReader
    from agno.vectordb.pgvector import PgVector

    db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

    knowledge = Knowledge(
        vector_db=PgVector(table_name="recipes_document_chunking", db_url=db_url),
    )

    asyncio.run(knowledge.ainsert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        reader=PDFReader(
            name="Document Chunking Reader",
            chunking_strategy=DocumentChunking(),
        ),
    ))

    agent = Agent(
        knowledge=knowledge,
        search_knowledge=True,
    )

    agent.print_response("How to make Thai curry?", markdown=True)
    ```
  </Step>

  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno sqlalchemy psycopg pgvector
    ```
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run the script">
    ```bash theme={null}
    python document_chunking.py
    ```
  </Step>
</Steps>

## Document Chunking Params

<Snippet file="chunking-document.mdx" />